We present a single, clinically feasible MRI sequence that provides robust T2-weighted imaging and water–fat–silicone separation for breast imaging with or without implants.

We introduce a practical technique to measure voxel-wise noise variance in AI-based MRI reconstructions, addressing a long-standing gap in accelerated MRI.

We present a novel diffusion-weighted imaging (DWI) technique that addresses two common challenges in standard echo-planar imaging (EPI): geometric distortion and residual fat signals.

We broadly categorize the different applications of AI in MRI and provide a framework for important considerations and challenges for prospectively deploying these promising tools in the clinic to enhance patient care.

We accelerate and improve the reconstruction of multi-shot diffusion-weighted MRI by an unrolled pipeline, in which the presumed regularization term is replaced by a U-Net.

Despite advances in accelerating MRI scans, diagnostic knee MRI protocols typically require upwards of 30 minutes of scanner time, which fundamentally limits patient throughput.

Acquisition of diffusion-weighted images along multiple directions is needed for deriving microstructural metrics from diffusion models, and may also provide valuable information for some clinical applications.

Deep learning based super-resolution (SR) is a computer vision method that can enhance the resolution of low-resolution images, which has recently been applied to MRI.

We apply automatic differentiation (widely used in deep learning) and a simple Bloch simulation to efficiently optimize any sequence that can be simulated.

Clinical knee MRI examinations typically utilize 2D imaging methods with thick slices, requiring upwards of 20-25 minutes of scan time, and yet not producing any quantitative information.